Neuromorphic systems, inspired by the complexity and functionality of the human brain, have gained interest in academic and industrial attention due to their unparalleled potential across a wide range of applications. While their capabilities herald innovation, it is imperative to underscore that these computational paradigms, analogous to their traditional counterparts, are not impervious to security threats. Although the exploration of neuromorphic methodologies for image and video processing has been rigorously pursued, the realm of neuromorphic audio processing remains in its early stages. Our results highlight the robustness and precision of our FPGA-based neuromorphic system. Specifically, our system showcases a commendable balance between desired signal and background noise, efficient spike rate encoding, and unparalleled resilience against adversarial attacks such as FGSM and PGD. A standout feature of our framework is its detection rate of 94%, which, when compared to other methodologies, underscores its greater capability in identifying and mitigating threats within 5.39 dB, a commendable SNR ratio. Furthermore, neuromorphic computing and hardware security serve many sensor domains in mission-critical and privacy-preserving applications.

翻译：神经形态系统受人类大脑复杂性和功能性的启发，因其在广泛应用中展现出的无与伦比的潜力，已获得学术界和工业界的广泛关注。尽管其能力预示着创新，但必须强调，这些计算范式与传统计算系统相似，并非对安全威胁免疫。虽然神经形态方法在图像和视频处理中的探索已得到深入研究，但神经形态音频处理领域仍处于早期阶段。我们的结果凸显了基于FPGA的神经形态系统的鲁棒性和精确性。具体而言，该系统在期望信号与背景噪声之间实现了令人瞩目的平衡，展示了高效尖峰率编码能力，并对FGSM和PGD等对抗性攻击具有无与伦比的恢复力。本框架的突出特点在于其94%的检测率，与其他方法相比，更凸显其以5.39 dB（一个值得称道的信噪比）识别和缓解威胁的卓越能力。此外，神经形态计算和硬件安全在关键任务和隐私保护应用中服务于众多传感器领域。